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Open nowPosted 71 days ago

校招-智能车控算法工程师

nio/campus867 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now校招-智能车控算法工程师nio/campus · Shanghai, Chinese Mainland

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on nio/campus's own form.

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The clock on this job

Early applications get read.

11.5% of postings close within 7 days. Measured by our own scanner across the market. nio/campus postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 71 days ago

nio/campus median: 1 days open

The posting

1、机理+深度学习融合建模：融合车辆动力学机理建模与深度学习技术，构建高精度、高泛化性的车辆物理仿真模型，支撑车控算法迭代研发。 2、多机器学习融合最优控制算法研发：基于监督学习完成车辆状态预估与基础控制策略开发，结合强化学习实现复杂工况自适应优化，达成车辆全域最优控制效果。 3、SOTIF对抗场景算法开发：面向智能辅助驾驶预期功能安全要求，研发对抗场景生成算法，覆盖极限、长尾风险场景，提升车控算法全场景安全性与合规性。

一、核心硬性要求 1、学历背景：2027届应届博士，控制、自动化、人工智能、车辆工程、应用数学等相关硬核理工专业，深耕智能控制、机器学习、车辆动力学相关方向；顶级成果硕士可破格遴选。 2、理论功底：具备扎实的博士层级理论基础，精通车辆动力学、系统辨识、非线性控制等机理建模理论；熟练掌握深度学习、监督学习、强化学习核心原理，深刻理解机理建模与数据驱动建模的融合逻辑，可独立解决算法精度、泛化性、工况适配等核心技术问题。 3、工程能力：具备算法从建模、仿真到落地的全流程研发能力。熟练使用Python、MATLAB/Simulink开展建模仿真，精通主流深度学习框架，可独立完成模型训练、算法开发与性能调优，实现理论算法工程化落地。 二、高阶优先条件 1、博士课题聚焦智能车控、车辆动力学优化、强化学习控制、系统辨识等相关方向，具备完整科研攻关经验； 2、拥有机理+数据融合建模、对抗场景生成、SOTIF功能安全、智能控制优化相关论文、专利或项目成果优先； 3、熟悉车控算法开发、场景库搭建、仿真验证体系，具备独立技术攻关、方案迭代与成果转化能力优先。

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